We compared our PromptGen and the plug-and-play generative model (PPGM) [1]} in terms of the inference latency. PPGM uses Langevin dynamics [2]} to optimize over the latent space at inference, while PromptGen samples images in a feed-forward manner. In this experiment, we used PPGM and our PromptGen to approximate the same EBM with CLIP energy.
Inference times were estimated on an NVIDIA RTX A4000 GPU.
Table REF  shows that our PromptGen has the highest performance and efficiency.
Compared to our PromptGen, PPGM requires 100\(\times \)  inference time to achieve comparable results.
Moreover, learning \(f_{\mathbf {\theta }}\)  in PromptGen requires training-time optimization, and this amortized optimization is useful when one wants to reuse a controlled distribution many times.
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